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relative_L2.py
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relative_L2.py
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import numpy as np
import scipy
import torch
import os
from pinn_model import *
import pandas as pd
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def load_data_feature(filename):
data_mat = scipy.io.loadmat(filename)
stack = data_mat['stack'] # N*4 (x,y,u,v)
x = stack[:, 0].reshape(-1, 1)
y = stack[:, 1].reshape(-1, 1)
t = stack[:, 2].reshape(-1, 1)
temp = np.concatenate((x, y, t), 1)
data_mean = np.mean(temp, axis=0).reshape(1, -1)
data_std = np.std(temp, axis=0).reshape(1, -1)
return data_mean, data_std
def load_full_points(filename):
# for each process, load data points within the extended boundary
# 每一个进程读取相应区域延拓边界内的数据点
# 读取原始数据
data_mat = scipy.io.loadmat(filename)
stack = data_mat['stack'] # N*4 (x,y,u,v)
x = stack[:, 0].reshape(-1, 1)
y = stack[:, 1].reshape(-1, 1)
t = stack[:, 2].reshape(-1, 1)
u = stack[:, 3].reshape(-1, 1)
v = stack[:, 4].reshape(-1, 1)
p = stack[:, 5].reshape(-1, 1)
low_bound = np.array([np.min(x), np.min(y), np.min(t)]).reshape(1, -1)
up_bound = np.array([np.max(x), np.max(y), np.max(t)]).reshape(1, -1)
temp = np.concatenate((x, y, t, u, v, p), 1)
x_ts = torch.tensor(x, dtype=torch.float32)
y_ts = torch.tensor(y, dtype=torch.float32)
t_ts = torch.tensor(t, dtype=torch.float32)
u_ts = torch.tensor(u, dtype=torch.float32)
v_ts = torch.tensor(v, dtype=torch.float32)
p_ts = torch.tensor(p, dtype=torch.float32)
return x_ts, y_ts, t_ts, u_ts, v_ts, p_ts, low_bound, up_bound
def compute_L2_norm(filename_train_data, filename_raw_data, filename_predict, norm_status="no_norm"):
# 预测值
x_raw, y_raw, t_raw, u_raw, v_raw, p_raw, low_bound, up_bound = load_full_points(filename_raw_data)
x_pre = x_raw.clone().detach().requires_grad_(True).to(device)
y_pre = y_raw.clone().detach().requires_grad_(True).to(device)
t_pre = t_raw.clone().detach().requires_grad_(True).to(device)
data_mean, data_std = load_data_feature(filename_train_data)
pinn_net = PINN_Net(layer_mat, data_mean, data_std, device)
pinn_net = pinn_net.to(device)
filename_load_model = filename_predict + '/NS_model_train.pt'
pinn_net.load_state_dict(torch.load(filename_load_model, map_location=device))
if norm_status == "no_norm":
u_pre, v_pre, p_pre = pinn_net.predict(x_pre, y_pre, t_pre)
else:
u_pre, v_pre, p_pre = pinn_net.predict_inner_norm(x_pre, y_pre, t_pre)
u_raw_mat = u_raw.numpy()
v_raw_mat = v_raw.numpy()
p_raw_mat = p_raw.numpy()
u_pre_mat = u_pre.cpu().detach().numpy()
v_pre_mat = v_pre.cpu().detach().numpy()
p_pre_mat = p_pre.cpu().detach().numpy()
# 处理数据
L2_u = (np.linalg.norm(u_pre_mat - u_raw_mat) / np.linalg.norm(u_raw_mat)).reshape(-1, 1)
L2_v = (np.linalg.norm(v_pre_mat - v_raw_mat) / np.linalg.norm(v_raw_mat)).reshape(-1, 1)
L2_p = (np.linalg.norm(p_pre_mat - p_raw_mat) / np.linalg.norm(p_raw_mat)).reshape(-1, 1)
L2_uvp_at_moment = np.concatenate((L2_u, L2_v, L2_p), axis=1)
return L2_uvp_at_moment
def L2_norm_at_moment(u_predicted, v_predicted, p_predicted, u_selected, v_selected, p_selected):
L2_u = (np.linalg.norm(u_predicted - u_selected) / np.linalg.norm(u_selected)).reshape(-1, 1)
L2_v = (np.linalg.norm(v_predicted - v_selected) / np.linalg.norm(v_selected)).reshape(-1, 1)
L2_p = (np.linalg.norm(p_predicted - p_selected) / np.linalg.norm(p_selected)).reshape(-1, 1)
L2_uvp_at_moment = np.concatenate((L2_u, L2_v, L2_p), axis=1)
return L2_uvp_at_moment
def rearrange_folders(matching_folders_list):
def compare_by_number(string):
# 提取字符串中的数字部分，并将其转换为整数进行比较
number = int(string[4:])
return number
rearranged_list = sorted(matching_folders_list, key=compare_by_number)
return rearranged_list
if __name__ == "__main__":
layer_mat_1 = [3, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 3] # 网络结构 neural network
layer_mat_2 = [3, 80, 80, 80, 80, 80, 3] # 网络结构 neural network
layer_mat_3 = [3, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 3] # 网络结构 neural network
layer_mat = layer_mat_1
filename_raw_data = './cylinder_Re3900_ke_all_100snaps.mat'
filename_train_data = './cylinder_Re3900_36points_100snaps.mat'
name_of_csv_set = ['exp_3900ke_1', 'exp_3900ke_2', 'exp_3900ke_3', 'exp_3900ke_4', 'exp_3900ke_5']
for name in name_of_csv_set:
folder_path = './data/' + name + '/write/'# 文件夹路径
prefix = 'step' # 文件夹名前缀
# 获取文件夹下所有文件夹名
folder_list = [folder for folder in os.listdir(folder_path) if os.path.isdir(os.path.join(folder_path, folder))]
# 筛选以指定前缀开头的文件夹名
matching_folders = [folder for folder in folder_list if folder.startswith(prefix)]
matching_folders = rearrange_folders(matching_folders)
print(matching_folders)
norm_status = "no_norm"
# norm_status = "inner_norm"
L2_set = np.empty((0, 3))
for dir_name in matching_folders:
file_predict = folder_path + dir_name
L2_norm = compute_L2_norm(filename_train_data, filename_raw_data, file_predict, norm_status)
L2_set = np.append(L2_set, L2_norm, axis=0)
# 存储L2范数信息
df = pd.DataFrame(L2_set, columns=["U", "V", "P"])
if not os.path.exists('./data_csv'):
os.makedirs('./data_csv')
filename_csv = './data_csv/non_dimensional_' + name + '.csv'
df.to_csv(filename_csv, index=False)
print('ok')